Analysis of GLDS-7 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
setwd('~/Documents/HTML_R/GLDS7')
R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.
if(file.exists('iDEP_core_functions.R'))
source('iDEP_core_functions.R') else
source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFile <- 'GLDS7_Expression.csv'
sampleInfoFile <- 'GLDS7_Sampleinfo.csv'
gldsMetadataFile <- 'GLDS7_Metadata.csv'
geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc.
geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db' # pathway database in SQL; can be GMT format
STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'
Parameters for reading data
input_missingValue <- 'geneMedian' #Missing values imputation method
input_dataFileFormat <- 1 #1- read counts, 2 FKPM/RPKM or DNA microarray
input_minCounts <- 0.5 #Min counts
input_NminSamples <- 1 #Minimum number of samples
input_countsLogStart <- 4 #Pseudo count for log CPM
input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Hypocotyl_FLT_Rep1 | Hypocotyl_FLT_Rep2 | Hypocotyl_FLT_Rep3 | Hypocotyl_FLT_Rep4 | Hypocotyl_FLT_Rep5 | Hypocotyl_GC_Rep1 | Hypocotyl_GC_Rep2 | Hypocotyl_GC_Rep3 | Hypocotyl_GC_Rep4 | Hypocotyl_GC_Rep5 | Root_FLT_Rep1 | Root_FLT_Rep2 | Root_FLT_Rep3 | Root_FLT_Rep4 | Root_FLT_Rep5 | Root_GC_Rep1 | Root_GC_Rep2 | Root_GC_Rep3 | Root_GC_Rep4 | Root_GC_Rep5 | Shoot_FLT_Rep1 | Shoot_FLT_Rep2 | Shoot_FLT_Rep3 | Shoot_FLT_Rep4 | Shoot_FLT_Rep5 | Shoot_GC_Rep1 | Shoot_GC_Rep2 | Shoot_GC_Rep3 | Shoot_GC_Rep4 | Shoot_GC_Rep5 | WholeSeedling_FLT_Rep1 | WholeSeedling_FLT_Rep2 | WholeSeedling_FLT_Rep3 | WholeSeedling_GC_Rep1 | WholeSeedling_GC_Rep2 | WholeSeedling_GC_Rep3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample.LongId | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep1.Array | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep2.Array | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep3.Array | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep4.Array | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep5.Array | Atha.WS.0.Col.0.Hypocotyl.GC.Rep1.Array | Atha.WS.0.Col.0.Hypocotyl.GC.Rep2.Array | Atha.WS.0.Col.0.Hypocotyl.GC.Rep3.Array | Atha.WS.0.Col.0.Hypocotyl.GC.Rep4.Array | Atha.WS.0.Col.0.Hypocotyl.GC.Rep5.Array | Atha.WS.0.Col.0.Root.FLT.Rep1.Array | Atha.WS.0.Col.0.Root.FLT.Rep2.Array | Atha.WS.0.Col.0.Root.FLT.Rep3.Array | Atha.WS.0.Col.0.Root.FLT.Rep4.Array | Atha.WS.0.Col.0.Root.FLT.Rep5.Array | Atha.WS.0.Col.0.Root.GC.Rep1.Array | Atha.WS.0.Col.0.Root.GC.Rep2.Array | Atha.WS.0.Col.0.Root.GC.Rep3.Array | Atha.WS.0.Col.0.Root.GC.Rep4.Array | Atha.WS.0.Col.0.Root.GC.Rep5.Array | Atha.WS.0.Col.0.Shoot.FLT.Rep1.Array | Atha.WS.0.Col.0.Shoot.FLT.Rep2.Array | Atha.WS.0.Col.0.Shoot.FLT.Rep3.Array | Atha.WS.0.Col.0.Shoot.FLT.Rep4.Array | Atha.WS.0.Col.0.Shoot.FLT.Rep5.Array | Atha.WS.0.Col.0.Shoot.GC.Rep1.Array | Atha.WS.0.Col.0.Shoot.GC.Rep2.Array | Atha.WS.0.Col.0.Shoot.GC.Rep3.Array | Atha.WS.0.Col.0.Shoot.GC.Rep4.Array | Atha.WS.0.Col.0.Shoot.GC.Rep5.Array | Atha.WS.0.Whole.Plant.FLT.Rep1.Array | Atha.WS.0.Whole.Plant.FLT.Rep2.Array | Atha.WS.0.Whole.Plant.FLT.Rep3.Array | Atha.WS.0.Whole.Plant.GC.Rep1.Array | Atha.WS.0.Whole.Plant.GC.Rep2.Array | Atha.WS.0.Whole.Plant.GC.Rep3.Array |
| Sample.Id | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep1 | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep2 | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep3 | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep4 | Atha.WS.0.Col.0.Hypocotyl.FLT.Rep5 | Atha.WS.0.Col.0.Hypocotyl.GC.Rep1 | Atha.WS.0.Col.0.Hypocotyl.GC.Rep2 | Atha.WS.0.Col.0.Hypocotyl.GC.Rep3 | Atha.WS.0.Col.0.Hypocotyl.GC.Rep4 | Atha.WS.0.Col.0.Hypocotyl.GC.Rep5 | Atha.WS.0.Col.0.Root.FLT.Rep1 | Atha.WS.0.Col.0.Root.FLT.Rep2 | Atha.WS.0.Col.0.Root.FLT.Rep3 | Atha.WS.0.Col.0.Root.FLT.Rep4 | Atha.WS.0.Col.0.Root.FLT.Rep5 | Atha.WS.0.Col.0.Root.GC.Rep1 | Atha.WS.0.Col.0.Root.GC.Rep2 | Atha.WS.0.Col.0.Root.GC.Rep3 | Atha.WS.0.Col.0.Root.GC.Rep4 | Atha.WS.0.Col.0.Root.GC.Rep5 | Atha.WS.0.Col.0.Shoot.FLT.Rep1 | Atha.WS.0.Col.0.Shoot.FLT.Rep2 | Atha.WS.0.Col.0.Shoot.FLT.Rep3 | Atha.WS.0.Col.0.Shoot.FLT.Rep4 | Atha.WS.0.Col.0.Shoot.FLT.Rep5 | Atha.WS.0.Col.0.Shoot.GC.Rep1 | Atha.WS.0.Col.0.Shoot.GC.Rep2 | Atha.WS.0.Col.0.Shoot.GC.Rep3 | Atha.WS.0.Col.0.Shoot.GC.Rep4 | Atha.WS.0.Col.0.Shoot.GC.Rep5 | Atha.WS.0.Whole.Plant.FLT.Rep1 | Atha.WS.0.Whole.Plant.FLT.Rep2 | Atha.WS.0.Whole.Plant.FLT.Rep3 | Atha.WS.0.Whole.Plant.GC.Rep1 | Atha.WS.0.Whole.Plant.GC.Rep2 | Atha.WS.0.Whole.Plant.GC.Rep3 |
| Sample.Name | Atha_WS-0_Col-0_Hypocotyl_FLT_Rep1 | Atha_WS-0_Col-0_Hypocotyl_FLT_Rep2 | Atha_WS-0_Col-0_Hypocotyl_FLT_Rep3 | Atha_WS-0_Col-0_Hypocotyl_FLT_Rep4 | Atha_WS-0_Col-0_Hypocotyl_FLT_Rep5 | Atha_WS-0_Col-0_Hypocotyl_GC_Rep1 | Atha_WS-0_Col-0_Hypocotyl_GC_Rep2 | Atha_WS-0_Col-0_Hypocotyl_GC_Rep3 | Atha_WS-0_Col-0_Hypocotyl_GC_Rep4 | Atha_WS-0_Col-0_Hypocotyl_GC_Rep5 | Atha_WS-0_Col-0_Root_FLT_Rep1 | Atha_WS-0_Col-0_Root_FLT_Rep2 | Atha_WS-0_Col-0_Root_FLT_Rep3 | Atha_WS-0_Col-0_Root_FLT_Rep4 | Atha_WS-0_Col-0_Root_FLT_Rep5 | Atha_WS-0_Col-0_Root_GC_Rep1 | Atha_WS-0_Col-0_Root_GC_Rep2 | Atha_WS-0_Col-0_Root_GC_Rep3 | Atha_WS-0_Col-0_Root_GC_Rep4 | Atha_WS-0_Col-0_Root_GC_Rep5 | Atha_WS-0_Col-0_Shoot_FLT_Rep1 | Atha_WS-0_Col-0_Shoot_FLT_Rep2 | Atha_WS-0_Col-0_Shoot_FLT_Rep3 | Atha_WS-0_Col-0_Shoot_FLT_Rep4 | Atha_WS-0_Col-0_Shoot_FLT_Rep5 | Atha_WS-0_Col-0_Shoot_GC_Rep1 | Atha_WS-0_Col-0_Shoot_GC_Rep2 | Atha_WS-0_Col-0_Shoot_GC_Rep3 | Atha_WS-0_Col-0_Shoot_GC_Rep4 | Atha_WS-0_Col-0_Shoot_GC_Rep5 | Atha_WS-0_Whole-Plant_FLT_Rep1 | Atha_WS-0_Whole-Plant_FLT_Rep2 | Atha_WS-0_Whole-Plant_FLT_Rep3 | Atha_WS-0_Whole-Plant_GC_Rep1 | Atha_WS-0_Whole-Plant_GC_Rep2 | Atha_WS-0_Whole-Plant_GC_Rep3 |
| GLDS | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
| Accession | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 | GLDS-7 |
| Hardware | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS | ABRS |
| Tissue | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Hypocotyl | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Shoot | Shoot | Shoot | Shoot | Shoot | Shoot | Shoot | Shoot | Shoot | Shoot | Whole seedling | Whole seedling | Whole seedling | Whole seedling | Whole seedling | Whole seedling |
| Age | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed | Col-0 & WS-0 mixed |
| Genotype | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT |
| Variety | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT | Col-0 & WS-0 mixed WT |
| Radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth |
| Gravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial |
| Developmental | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling hypocotyl | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling shoots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots | 12 day old seedling roots |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light | White light |
| Assay..RNAseq. | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling |
| Temperature | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 |
| Treatment.type | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight |
| Treatment.intensity | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Treament.timing | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Preservation.Method. | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater | RNALater |
readData.out <- readData(inputFile)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable( head(readData.out$data) ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Hypocotyl_FLT_Rep1 | Hypocotyl_FLT_Rep2 | Hypocotyl_FLT_Rep3 | Hypocotyl_FLT_Rep4 | Hypocotyl_FLT_Rep5 | Hypocotyl_GC_Rep1 | Hypocotyl_GC_Rep2 | Hypocotyl_GC_Rep3 | Hypocotyl_GC_Rep4 | Hypocotyl_GC_Rep5 | Root_FLT_Rep1 | Root_FLT_Rep2 | Root_FLT_Rep3 | Root_FLT_Rep4 | Root_FLT_Rep5 | Root_GC_Rep1 | Root_GC_Rep2 | Root_GC_Rep3 | Root_GC_Rep4 | Root_GC_Rep5 | Shoot_FLT_Rep1 | Shoot_FLT_Rep2 | Shoot_FLT_Rep3 | Shoot_FLT_Rep4 | Shoot_FLT_Rep5 | Shoot_GC_Rep1 | Shoot_GC_Rep2 | Shoot_GC_Rep3 | Shoot_GC_Rep4 | Shoot_GC_Rep5 | WholeSeedling_FLT_Rep1 | WholeSeedling_FLT_Rep2 | WholeSeedling_FLT_Rep3 | WholeSeedling_GC_Rep1 | WholeSeedling_GC_Rep2 | WholeSeedling_GC_Rep3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT3G01500 | 2.797288 | 2.801275 | 2.801971 | 2.801092 | 2.801110 | 2.577317 | 2.798415 | 2.800664 | 2.795681 | 2.799021 | 2.800339 | 2.575584 | 2.794532 | 2.802569 | 2.579733 | 2.575710 | 2.800731 | 2.798693 | 2.801759 | 2.804228 | 3.911239 | 3.920361 | 3.909440 | 3.913533 | 3.807355 | 3.927754 | 3.915216 | 3.812816 | 3.812959 | 3.813825 | 3.715466 | 3.722478 | 3.607228 | 3.715466 | 3.723016 | 3.718766 |
| AT5G14740 | 2.797288 | 2.992904 | 2.801971 | 2.801092 | 2.801110 | 2.797518 | 2.798415 | 2.800664 | 2.575891 | 2.799021 | 2.800339 | 2.575584 | 2.574999 | 2.802569 | 2.579733 | 2.795448 | 2.800731 | 2.798693 | 2.801759 | 2.804228 | 3.811591 | 3.920361 | 3.809838 | 3.813825 | 3.807355 | 3.827680 | 3.815465 | 3.812816 | 3.812959 | 3.813825 | 3.599435 | 3.722478 | 3.607228 | 3.715466 | 3.606709 | 3.718766 |
| AT5G46890 | 2.577138 | 2.580236 | 2.580777 | 2.580094 | 2.580108 | 2.577317 | 2.578014 | 2.579761 | 2.575891 | 2.578485 | 3.795643 | 3.787183 | 3.679669 | 3.799369 | 3.796125 | 3.787455 | 3.796299 | 3.792889 | 3.897302 | 3.901536 | 2.809898 | 3.180141 | 2.808845 | 2.587985 | 2.584963 | 2.594483 | 2.812226 | 2.587513 | 2.810720 | 2.587985 | 3.181995 | 3.341048 | 3.341982 | 3.181995 | 3.480197 | 3.476286 |
| AT2G10940 | 2.797288 | 2.801275 | 2.801971 | 2.580094 | 2.580108 | 2.577317 | 2.578014 | 2.800664 | 2.795681 | 2.799021 | 2.579509 | 2.575584 | 2.574999 | 2.581241 | 2.800627 | 2.575710 | 2.800731 | 2.578230 | 2.801759 | 2.804228 | 3.811591 | 3.820477 | 3.809838 | 3.813825 | 3.700440 | 3.827680 | 3.815465 | 3.812816 | 3.812959 | 3.813825 | 3.599435 | 3.606191 | 3.607228 | 3.599435 | 3.606709 | 3.602614 |
| AT1G09310 | 3.684142 | 3.797207 | 3.691732 | 3.690310 | 3.690339 | 3.684516 | 3.685971 | 3.689616 | 3.681535 | 3.571978 | 2.800339 | 2.985909 | 2.985029 | 3.163717 | 2.800627 | 2.795448 | 2.800731 | 2.989889 | 2.993469 | 2.996351 | 4.004447 | 4.013775 | 4.002607 | 4.006793 | 4.000000 | 3.927754 | 4.008514 | 4.005733 | 4.005884 | 4.006793 | 4.016271 | 4.023860 | 4.025025 | 4.016271 | 4.024442 | 4.019843 |
| ATCG00680 | 3.156861 | 3.162039 | 2.801971 | 2.992691 | 2.801110 | 3.157161 | 2.989565 | 2.992191 | 2.986371 | 2.990273 | 2.800339 | 2.795285 | 2.794532 | 2.802569 | 2.800627 | 2.795448 | 2.800731 | 2.798693 | 2.801759 | 2.804228 | 3.463206 | 3.713160 | 3.461644 | 3.591002 | 3.321928 | 3.827680 | 3.708301 | 3.464297 | 3.705872 | 3.465197 | 4.016271 | 4.023860 | 4.025025 | 4.016271 | 4.024442 | 4.019843 |
readSampleInfo.out <- readSampleInfo(sampleInfoFile)
kable( readSampleInfo.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Gravity | Tissue | |
|---|---|---|
| Hypocotyl_FLT_Rep1 | Microgravity | Hypocotyl |
| Hypocotyl_FLT_Rep2 | Microgravity | Hypocotyl |
| Hypocotyl_FLT_Rep3 | Microgravity | Hypocotyl |
| Hypocotyl_FLT_Rep4 | Microgravity | Hypocotyl |
| Hypocotyl_FLT_Rep5 | Microgravity | Hypocotyl |
| Hypocotyl_GC_Rep1 | Terrestrial | Hypocotyl |
| Hypocotyl_GC_Rep2 | Terrestrial | Hypocotyl |
| Hypocotyl_GC_Rep3 | Terrestrial | Hypocotyl |
| Hypocotyl_GC_Rep4 | Terrestrial | Hypocotyl |
| Hypocotyl_GC_Rep5 | Terrestrial | Hypocotyl |
| Root_FLT_Rep1 | Microgravity | Roots |
| Root_FLT_Rep2 | Microgravity | Roots |
| Root_FLT_Rep3 | Microgravity | Roots |
| Root_FLT_Rep4 | Microgravity | Roots |
| Root_FLT_Rep5 | Microgravity | Roots |
| Root_GC_Rep1 | Terrestrial | Roots |
| Root_GC_Rep2 | Terrestrial | Roots |
| Root_GC_Rep3 | Terrestrial | Roots |
| Root_GC_Rep4 | Terrestrial | Roots |
| Root_GC_Rep5 | Terrestrial | Roots |
| Shoot_FLT_Rep1 | Microgravity | Shoot |
| Shoot_FLT_Rep2 | Microgravity | Shoot |
| Shoot_FLT_Rep3 | Microgravity | Shoot |
| Shoot_FLT_Rep4 | Microgravity | Shoot |
| Shoot_FLT_Rep5 | Microgravity | Shoot |
| Shoot_GC_Rep1 | Terrestrial | Shoot |
| Shoot_GC_Rep2 | Terrestrial | Shoot |
| Shoot_GC_Rep3 | Terrestrial | Shoot |
| Shoot_GC_Rep4 | Terrestrial | Shoot |
| Shoot_GC_Rep5 | Terrestrial | Shoot |
| WholeSeedling_FLT_Rep1 | Microgravity | WholeSeedling |
| WholeSeedling_FLT_Rep2 | Microgravity | WholeSeedling |
| WholeSeedling_FLT_Rep3 | Microgravity | WholeSeedling |
| WholeSeedling_GC_Rep1 | Terrestrial | WholeSeedling |
| WholeSeedling_GC_Rep2 | Terrestrial | WholeSeedling |
| WholeSeedling_GC_Rep3 | Terrestrial | WholeSeedling |
input_selectOrg ="NEW"
input_selectGO <- 'GOBP' #Gene set category
input_noIDConversion = TRUE
allGeneInfo.out <- geneInfo(geneInfoFile)
converted.out = NULL
convertedData.out <- convertedData()
nGenesFilter()
## [1] "16156 genes in 36 samples. 16156 genes passed filter.\n Original gene IDs used."
convertedCounts.out <- convertedCounts() # converted counts, just for compatibility
# Read counts per library
parDefault = par()
par(mar=c(12,4,2,2))
# barplot of total read counts
x <- readData.out$rawCounts
groups = as.factor( detectGroups(colnames(x ) ) )
if(nlevels(groups)<=1 | nlevels(groups) >20 )
col1 = 'green' else
col1 = rainbow(nlevels(groups))[ groups ]
barplot( colSums(x)/1e6,
col=col1,las=3, main="Total read counts (millions)")
readCountsBias() # detecting bias in sequencing depth
## [1] 5.679193e-14
## [1] 0.842966
## [1] 1.824797e-16
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 5.68e-14 ) based on ANOVA. Total read counts seem to be correlated with factor Tissue (p= 1.82e-16 ). "
# Box plot
x = readData.out$data
boxplot(x, las = 2, col=col1,
ylab='Transformed expression levels',
main='Distribution of transformed data')
#Density plot
par(parDefault)
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
densityPlot()
# Scatter plot of the first two samples
plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2],
main='Scatter plot of first two samples')
####plot gene or gene family
input_selectOrg ="BestMatch"
input_geneSearch <- 'HOXA' #Gene ID for searching
genePlot()
## NULL
input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar?
geneBarPlotError()
## NULL
# hierarchical clustering tree
x <- readData.out$data
maxGene <- apply(x,1,max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1] ) ,]
plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle")
#Correlation matrix
input_labelPCC <- TRUE #Show correlation coefficient?
correlationMatrix()
# Parameters for heatmap
input_nGenes <- 1000 #Top genes for heatmap
input_geneCentering <- TRUE #centering genes ?
input_sampleCentering <- FALSE #Center by sample?
input_geneNormalize <- FALSE #Normalize by gene?
input_sampleNormalize <- FALSE #Normalize by sample?
input_noSampleClustering <- FALSE #Use original sample order
input_heatmapCutoff <- 4 #Remove outliers beyond number of SDs
input_distFunctions <- 1 #which distant funciton to use
input_hclustFunctions <- 1 #Linkage type
input_heatColors1 <- 1 #Colors
input_selectFactorsHeatmap <- 'Gravity' #Sample coloring factors
png('heatmap.png', width = 10, height = 15, units = 'in', res = 300)
staticHeatmap()
dev.off()
## png
## 2
[heatmap] (heatmap.png)
heatmapPlotly() # interactive heatmap using Plotly
input_nGenesKNN <- 2000 #Number of genes fro k-Means
input_nClusters <- 4 #Number of clusters
maxGeneClustering = 12000
input_kmeansNormalization <- 'geneMean' #Normalization
input_KmeansReRun <- 0 #Random seed
distributionSD() #Distribution of standard deviations
KmeansNclusters() #Number of clusters
Kmeans.out = Kmeans() #Running K-means
KmeansHeatmap() #Heatmap for k-Means
#Read gene sets for enrichment analysis
sqlite <- dbDriver('SQLite')
input_selectGO3 <- 'GOBP' #Gene set category
input_minSetSize <- 15 #Min gene set size
input_maxSetSize <- 2000 #Max gene set size
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO3,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
# Alternatively, users can use their own GMT files by
#GeneSets.out <- readGMTRobust('somefile.GMT')
results <- KmeansGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 3.75e-139 | 124 | Photosynthesis |
| 1.43e-90 | 76 | Photosynthesis, light reaction | |
| 4.31e-60 | 91 | Generation of precursor metabolites and energy | |
| 4.60e-50 | 165 | Response to abiotic stimulus | |
| 3.48e-43 | 34 | Photosynthetic electron transport chain | |
| 6.49e-37 | 83 | Response to light stimulus | |
| 4.21e-36 | 118 | Oxidation-reduction process | |
| 1.01e-35 | 83 | Response to radiation | |
| 3.19e-33 | 124 | Organonitrogen compound biosynthetic process | |
| 1.32e-30 | 52 | Plastid organization | |
| B | 3.19e-08 | 50 | Nucleobase-containing compound biosynthetic process |
| 3.68e-08 | 46 | Regulation of nucleobase-containing compound metabolic process | |
| 1.15e-07 | 41 | Regulation of transcription, DNA-templated | |
| 1.15e-07 | 47 | Regulation of gene expression | |
| 1.15e-07 | 43 | Regulation of RNA metabolic process | |
| 1.15e-07 | 41 | Regulation of nucleic acid-templated transcription | |
| 1.15e-07 | 41 | Regulation of RNA biosynthetic process | |
| 1.27e-07 | 42 | Transcription, DNA-templated | |
| 1.33e-07 | 42 | Nucleic acid-templated transcription | |
| 1.57e-07 | 42 | RNA biosynthetic process | |
| C | 6.87e-19 | 29 | Cellular response to decreased oxygen levels |
| 6.87e-19 | 72 | Cellular response to chemical stimulus | |
| 6.87e-19 | 29 | Cellular response to oxygen levels | |
| 6.87e-19 | 29 | Cellular response to hypoxia | |
| 8.23e-19 | 86 | Response to abiotic stimulus | |
| 1.18e-17 | 29 | Response to hypoxia | |
| 1.56e-17 | 29 | Response to decreased oxygen levels | |
| 1.57e-17 | 29 | Response to oxygen levels | |
| 2.58e-15 | 76 | Response to organic substance | |
| 2.58e-15 | 67 | Response to oxygen-containing compound | |
| D | 3.93e-32 | 43 | Detoxification |
| 5.52e-28 | 47 | Response to toxic substance | |
| 1.40e-24 | 43 | Secondary metabolic process | |
| 2.83e-23 | 33 | Cellular response to toxic substance | |
| 3.32e-23 | 86 | Oxidation-reduction process | |
| 3.39e-23 | 26 | Antibiotic catabolic process | |
| 1.25e-22 | 24 | Hydrogen peroxide catabolic process | |
| 2.77e-22 | 31 | Cellular detoxification | |
| 4.94e-21 | 25 | Hydrogen peroxide metabolic process | |
| 6.91e-21 | 29 | Cellular oxidant detoxification |
input_seedTSNE <- 0 #Random seed for t-SNE
input_colorGenes <- TRUE #Color genes in t-SNE plot?
tSNEgenePlot() #Plot genes using t-SNE
input_selectFactors <- 'Gravity' #Factor coded by color
input_selectFactors2 <- 'Tissue' #Factor coded by shape
input_tsneSeed2 <- 0 #Random seed for t-SNE
#PCA, MDS and t-SNE plots
PCAplot()
MDSplot()
tSNEplot()
#Read gene sets for pathway analysis using PGSEA on principal components
input_selectGO6 <- 'GOBP'
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO6,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
PCApathway() # Run PGSEA analysis
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
cat( PCA2factor() ) #The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Tissue (p=4.10e-31).
## PC2 is correlated with Tissue (p=2.89e-36).
## PC3 is correlated with Tissue (p=1.83e-25).
## PC4 is correlated with Gravity (p=7.19e-05).
input_CountsDEGMethod <- 2 #DESeq2= 3,limma-voom=2,limma-trend=1
input_limmaPval <- 0.1 #FDR cutoff
input_limmaFC <- 2 #Fold-change cutoff
input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial' #Selected comparisons
input_selectFactorsModel <- 'Gravity' #Selected comparisons
input_selectInteractions <- NULL #Selected comparisons
input_selectBlockFactorsModel <- NULL #Selected comparisons
factorReferenceLevels.out <- NULL
limma.out <- limma()
DEG.data.out <- DEG.data()
limma.out$comparisons
## [1] "Microgravity-Terrestrial"
input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
input_UpDownRegulated <- FALSE #Split up and down regulated genes
vennPlot() # Venn diagram
sigGeneStats() # number of DEGs as figure
sigGeneStatsTable() # number of DEGs as table
## Comparisons Up Down
## Microgravity-Terrestrial Microgravity-Terrestrial 0 0
input_selectContrast <- 'Terrestrial-Microgravity' #Selected comparisons
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap() # heatmap for DEGs in selected comparison
## Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'
# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv')
write.csv(DEG.data(),'DEG.data.csv' )
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'GOBP' #Gene set category
geneListData.out <- geneListData()
volcanoPlot()
scatterPlot()
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
MAplot()
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
geneListGOTable.out <- geneListGOTable()
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO2,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_removeRedudantSets <- TRUE #Remove highly redundant gene sets?
results <- geneListGO() #Enrichment analysis
## Error in if (dim(results1)[2] == 1) return(results1) else {: argument is of length zero
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 3.75e-139 | 124 | Photosynthesis |
| 1.43e-90 | 76 | Photosynthesis, light reaction | |
| 4.31e-60 | 91 | Generation of precursor metabolites and energy | |
| 4.60e-50 | 165 | Response to abiotic stimulus | |
| 3.48e-43 | 34 | Photosynthetic electron transport chain | |
| 6.49e-37 | 83 | Response to light stimulus | |
| 4.21e-36 | 118 | Oxidation-reduction process | |
| 1.01e-35 | 83 | Response to radiation | |
| 3.19e-33 | 124 | Organonitrogen compound biosynthetic process | |
| 1.32e-30 | 52 | Plastid organization | |
| B | 3.19e-08 | 50 | Nucleobase-containing compound biosynthetic process |
| 3.68e-08 | 46 | Regulation of nucleobase-containing compound metabolic process | |
| 1.15e-07 | 41 | Regulation of transcription, DNA-templated | |
| 1.15e-07 | 47 | Regulation of gene expression | |
| 1.15e-07 | 43 | Regulation of RNA metabolic process | |
| 1.15e-07 | 41 | Regulation of nucleic acid-templated transcription | |
| 1.15e-07 | 41 | Regulation of RNA biosynthetic process | |
| 1.27e-07 | 42 | Transcription, DNA-templated | |
| 1.33e-07 | 42 | Nucleic acid-templated transcription | |
| 1.57e-07 | 42 | RNA biosynthetic process | |
| C | 6.87e-19 | 29 | Cellular response to decreased oxygen levels |
| 6.87e-19 | 72 | Cellular response to chemical stimulus | |
| 6.87e-19 | 29 | Cellular response to oxygen levels | |
| 6.87e-19 | 29 | Cellular response to hypoxia | |
| 8.23e-19 | 86 | Response to abiotic stimulus | |
| 1.18e-17 | 29 | Response to hypoxia | |
| 1.56e-17 | 29 | Response to decreased oxygen levels | |
| 1.57e-17 | 29 | Response to oxygen levels | |
| 2.58e-15 | 76 | Response to organic substance | |
| 2.58e-15 | 67 | Response to oxygen-containing compound | |
| D | 3.93e-32 | 43 | Detoxification |
| 5.52e-28 | 47 | Response to toxic substance | |
| 1.40e-24 | 43 | Secondary metabolic process | |
| 2.83e-23 | 33 | Cellular response to toxic substance | |
| 3.32e-23 | 86 | Oxidation-reduction process | |
| 3.39e-23 | 26 | Antibiotic catabolic process | |
| 1.25e-22 | 24 | Hydrogen peroxide catabolic process | |
| 2.77e-22 | 31 | Cellular detoxification | |
| 4.94e-21 | 25 | Hydrogen peroxide metabolic process | |
| 6.91e-21 | 29 | Cellular oxidant detoxification |
STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.
STRING10_species = read.csv(STRING10_speciesFile)
ix = grep('Arabidopsis thaliana', STRING10_species$official_name )
findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
findTaxonomyID.out
## [1] 3702
Enrichment analysis using STRING
STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in names(x) <- value: 'names' attribute [2] must be the same length as the vector [1]
input_STRINGdbGO <- 'Process' #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
results <- stringDB_GO_enrichmentData() # enrichment using STRING
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
|
PPI network retrieval and analysis
input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis
stringDB_network1(1) #Show PPI network
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found
Generating interactive PPI
write(stringDB_network_link(), 'PPI_results.html') # write results to html file
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
browseURL('PPI_results.html') # open in browser
input_selectContrast1 <- 'Terrestrial-Microgravity' #select Comparison
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'GOBP' #Gene set category
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15 #Min size for gene set
input_maxSetSize <- 2000 #Max size for gene set
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_pathwayPvalCutoff <- 0.2 #FDR cutoff
input_nPathwayShow <- 30 #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1 #FDR to remove genes
input_pathwayMethod = 1 # 1 GAGE
gagePathwayData.out <- gagePathwayData() # pathway analysis using GAGE
results <- gagePathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GAGE analysis: Terrestrial vs Microgravity | statistic | Genes | adj.Pval |
|---|---|---|---|---|
| Up | Cellular response to decreased oxygen levels | 10.127 | 176 | 5.4e-18 |
| Cellular response to oxygen levels | 10.127 | 176 | 5.4e-18 | |
| Cellular response to hypoxia | 10.0985 | 175 | 5.4e-18 | |
| Response to hypoxia | 9.6969 | 197 | 3.0e-17 | |
| Response to decreased oxygen levels | 9.6906 | 200 | 3.0e-17 | |
| Response to oxygen levels | 9.6474 | 201 | 3.2e-17 | |
| Response to chitin | 7.6128 | 104 | 4.5e-10 | |
| Response to drug | 7.0437 | 482 | 4.9e-10 | |
| Response to organonitrogen compound | 5.8554 | 212 | 1.2e-06 | |
| Response to nitrogen compound | 5.4244 | 271 | 9.3e-06 | |
| Immune system process | 5.1485 | 345 | 3.0e-05 | |
| Response to wounding | 4.9489 | 151 | 1.0e-04 | |
| Immune response | 4.9133 | 312 | 9.0e-05 | |
| Response to bacterium | 4.83 | 400 | 1.1e-04 | |
| Response to antibiotic | 4.8046 | 255 | 1.3e-04 | |
| Innate immune response | 4.6716 | 303 | 2.2e-04 | |
| Defense response to bacterium | 4.6489 | 345 | 2.2e-04 | |
| Ethylene-activated signaling pathway | 4.3021 | 141 | 1.3e-03 | |
| Response to ethylene | 4.0945 | 227 | 2.6e-03 | |
| Regulation of defense response | 4.0017 | 218 | 3.5e-03 | |
| Regulation of response to stress | 3.9519 | 322 | 3.8e-03 | |
| Response to organic cyclic compound | 3.948 | 291 | 3.8e-03 | |
| Response to temperature stimulus | 3.8799 | 493 | 4.5e-03 | |
| Response to salicylic acid | 3.8412 | 163 | 5.5e-03 | |
| Response to heat | 3.8272 | 188 | 5.5e-03 | |
| Positive regulation of cellular biosynthetic process | 3.8163 | 457 | 5.5e-03 | |
| Positive regulation of biosynthetic process | 3.7696 | 468 | 6.0e-03 | |
| Positive regulation of macromolecule biosynthetic process | 3.7372 | 435 | 6.6e-03 | |
| Positive regulation of nucleobase-containing compound metabolic process | 3.7037 | 445 | 7.1e-03 | |
| Positive regulation of RNA metabolic process | 3.703 | 412 | 7.1e-03 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
input_pathwayMethod = 3 # 1 fgsea
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
results <- fgseaPathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GSEA analysis: Terrestrial vs Microgravity | NES | Genes | adj.Pval |
|---|---|---|---|---|
| Up | Cellular response to hypoxia | 2.9882 | 175 | 3.3e-03 |
| Cellular response to decreased oxygen levels | 2.981 | 176 | 3.3e-03 | |
| Cellular response to oxygen levels | 2.981 | 176 | 3.3e-03 | |
| Response to hypoxia | 2.9627 | 197 | 3.3e-03 | |
| Response to decreased oxygen levels | 2.9371 | 200 | 3.3e-03 | |
| Response to oxygen levels | 2.933 | 201 | 3.3e-03 | |
| Response to chitin | 2.8501 | 104 | 3.4e-03 | |
| Response to organonitrogen compound | 2.5552 | 212 | 3.3e-03 | |
| Response to drug | 2.4517 | 482 | 3.3e-03 | |
| Response to nitrogen compound | 2.3891 | 271 | 3.3e-03 | |
| Response to wounding | 2.3397 | 151 | 3.3e-03 | |
| Ethylene-activated signaling pathway | 2.2296 | 141 | 3.3e-03 | |
| Response to antibiotic | 2.2239 | 255 | 3.3e-03 | |
| Defense response to bacterium, incompatible interaction | 2.1765 | 40 | 3.5e-03 | |
| Response to salicylic acid | 2.1524 | 163 | 3.3e-03 | |
| Cellular response to ethylene stimulus | 2.0978 | 157 | 3.3e-03 | |
| Immune system process | 2.0893 | 345 | 3.3e-03 | |
| Response to ethylene | 2.0884 | 227 | 3.3e-03 | |
| Response to hydrogen peroxide | 2.0764 | 65 | 3.4e-03 | |
| Regulation of secondary metabolic process | 2.0661 | 39 | 6.1e-03 | |
| Immune response | 2.0583 | 312 | 3.3e-03 | |
| Response to jasmonic acid | 2.0554 | 179 | 3.3e-03 | |
| Defense response to other organism | 2.0504 | 598 | 3.3e-03 | |
| Phosphorelay signal transduction system | 2.0367 | 181 | 3.3e-03 | |
| Regulation of DNA-templated transcription in response to stress | 2.0314 | 25 | 3.6e-03 | |
| Defense response to bacterium | 2.0279 | 345 | 3.3e-03 | |
| Defense response | 2.0218 | 936 | 3.3e-03 | |
| Response to bacterium | 2.0191 | 400 | 3.3e-03 | |
| Innate immune response | 2.0171 | 303 | 3.3e-03 | |
| Regulation of defense response | 2.0154 | 218 | 3.3e-03 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
PGSEAplot() # pathway analysis using PGSEA
## Error in findContrastSamples(input_selectContrast1, colnames(convertedData.out), : object 'c.out' not found
input_selectContrast2 <- 'Terrestrial-Microgravity' #select Comparison
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1 #FDR to filter genes
input_limmaFCViz <- 2 #FDR to filter genes
genomePlotly() # shows fold-changes on the genome
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ...
biclustering.out = biclustering() # run analysis
input_selectBicluster <- 1 #select a cluster
biclustHeatmap() # heatmap for selected cluster
input_selectGO4 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO4,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
results <- geneListBclustGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.4e-83 | 100 | Photosynthesis |
| 2.7e-55 | 171 | Oxidation-reduction process |
| 1.9e-50 | 58 | Photosynthesis, light reaction |
| 9.4e-40 | 83 | Generation of precursor metabolites and energy |
| 1.8e-31 | 167 | Response to abiotic stimulus |
| 7.1e-26 | 27 | Photosynthetic electron transport chain |
| 1.4e-25 | 46 | Electron transport chain |
| 3.5e-24 | 43 | Drug catabolic process |
| 6.1e-24 | 82 | Drug metabolic process |
| 8.5e-24 | 24 | Protein-chromophore linkage |
input_mySoftPower <- 5 #SoftPower to cutoff
input_nGenesNetwork <- 1000 #Number of top genes
input_minModuleSize <- 20 #Module size minimum
wgcna.out = wgcna() # run WGCNA
## Warning: executing %dopar% sequentially: no parallel backend registered
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.859000 2.92000 0.8180 576.0 615.0 722.0
## 2 2 0.870000 1.41000 0.8360 409.0 444.0 578.0
## 3 3 0.818000 0.87600 0.7670 314.0 342.0 487.0
## 4 4 0.716000 0.57000 0.6390 252.0 274.0 420.0
## 5 5 0.492000 0.36100 0.3680 207.0 225.0 367.0
## 6 6 0.257000 0.21500 0.1590 174.0 188.0 324.0
## 7 7 0.057500 0.09630 0.0361 148.0 159.0 289.0
## 8 8 0.000405 0.00925 0.0542 128.0 135.0 259.0
## 9 9 0.037200 -0.09300 0.1300 111.0 118.0 234.0
## 10 10 0.121000 -0.17700 0.3040 97.4 101.0 212.0
## 11 12 0.245000 -0.29600 0.5140 76.2 76.7 177.0
## 12 14 0.353000 -0.40800 0.6580 60.9 59.9 151.0
## 13 16 0.435000 -0.51100 0.7450 49.4 46.9 130.0
## 14 18 0.499000 -0.61100 0.8120 40.7 37.5 113.0
## 15 20 0.536000 -0.67700 0.8560 33.9 30.2 99.4
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower() # soft power curve
modulePlot() # plot modules
listWGCNA.Modules.out = listWGCNA.Modules() #modules
input_selectGO5 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO5,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_selectWGCNA.Module <- 'Entire network' #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4 #Number of top genes
moduleNetwork() # show network of top genes in selected module
## softConnectivity: FYI: connecitivty of genes with less than 12 valid samples will be returned as NA.
## ..calculating connectivities..
input_removeRedudantSets <- TRUE #Remove redundant gene sets
results <- networkModuleGO() #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.4e-83 | 100 | Photosynthesis |
| 2.7e-55 | 171 | Oxidation-reduction process |
| 1.9e-50 | 58 | Photosynthesis, light reaction |
| 9.4e-40 | 83 | Generation of precursor metabolites and energy |
| 1.8e-31 | 167 | Response to abiotic stimulus |
| 7.1e-26 | 27 | Photosynthetic electron transport chain |
| 1.4e-25 | 46 | Electron transport chain |
| 3.5e-24 | 43 | Drug catabolic process |
| 6.1e-24 | 82 | Drug metabolic process |
| 8.5e-24 | 24 | Protein-chromophore linkage |